Analyzing the cardiovascular system condition via Electrocardiography (ECG)
is a common and highly effective approach, and it has been practiced and
perfected over many decades. ECG sensing is non-invasive and relatively easy to
acquire, and yet it is still cumbersome for holter monitoring tests that may
span over hours and even days. A possible alternative in this context is
Photoplethysmography (PPG): An optically-based signal that measures blood
volume fluctuations, as typically sensed by conventional ``wearable devices''.
While PPG presents clear advantages in acquisition, convenience, and
cost-effectiveness, ECG provides more comprehensive information, allowing for a
more precise detection of heart conditions. This implies that a conversion from
PPG to ECG, as recently discussed in the literature, inherently involves an
unavoidable level of uncertainty. In this paper we introduce a novel
methodology for addressing the PPG-2-ECG conversion, and offer an enhanced
classification of cardiovascular conditions using the given PPG, all while
taking into account the uncertainties arising from the conversion process. We
provide a mathematical justification for our proposed computational approach,
and present empirical studies demonstrating its superior performance compared
to state-of-the-art baseline methods.

通过光电式容积脉搏图对心血管系统状况进行分析已经成为一种常见且高效的方法，本文提出了一种新的方法论，基于这种方法可以将光电式容积脉搏图转换为心电图，通过计算机模型进行心血管疾病的更准确分类。

基于扩散模型的不确定性感知的 PPG - 至 - ECG 技术用于改进心血管诊断

Uncertainty-Aware PPG-2-ECG for Enhanced Cardiovascular Diagnosis using  Diffusion Models

Segmentation of the left atrium and deriving its size can help to predict and
detect various cardiovascular conditions. Automation of this process in 3D
Ultrasound image data is desirable, since manual delineations are
time-consuming, challenging and observer-dependent. Convolutional neural
networks have made improvements in computer vision and in medical image
analysis. They have successfully been applied to segmentation tasks and were
extended to work on volumetric data. In this paper we introduce a combined
deep-learning based approach on volumetric segmentation in Ultrasound
acquisitions with incorporation of prior knowledge about left atrial shape and
imaging device. The results show, that including a shape prior helps the domain
adaptation and the accuracy of segmentation is further increased with
adversarial learning.

该研究介绍了一种基于深度学习技术的左心房三维超声图像分割方法，结合了左心房形状和影像设备的先验知识，实验结果表明该方法能够提高分割准确度，能够帮助预测和发现各种心血管疾病。